Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials: On-Demand Oral Presentations
Program Organizers: Yu-chen Karen Chen-Wiegart, Stony Brook University / Brookhaven National Laboratory
Monday 8:00 AM
March 14, 2022
Room: Special Topics
Location: On-Demand Room
Autonomous X-ray Scattering for the Study of Non-equilibrium Self-assembly: Kevin Yager1; 1Brookhaven National Laboratory
Block copolymer thin films self-assemble into canonical morphologies. The diversity of structures can be increased by leveraging non-equilibrium effects. For instance, pathway-dependent aspects of assembly can be exploited to stabilize non-native motifs. However, this greatly increases the corresponding material discovery task, since the space of possible processing pathways is enormous. Autonomous experimentation approaches, which leverage machine-learning to drive a measurement loop and optimally explore a given problem, are a potential solution to this enormous challenge. This talk will discuss the ongoing development of autonomous experimentation at a synchrotron x-ray scattering beamline, using non-equilibrium block copolymer assembly as a key example.
Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices: Aaron Michelson1; Brian Minevich1; Hamed Emamy1; Xiaojing Huang2; Yong Chu2; Hanfei Yan2; Oleg Gang1; 1Columbia University; 2National Light Source II, BNL
We applied a scanning hard x-ray microscopy to volumetrically characterize DNA-assembled nanoparticle superlattices with a single-particle precision and to reveal the positions of about 104 individual gold nanoparticles (AuNP) within a superlattice at 7nm resolution. The lattice is assembled from 20-nm AuNP and DNA tetrahedra frames whose vertices were complementary encoded with DNA sequences grafted to AuNP surface. The formed lattice was templated by silica, thus creating a robust 3D architecture. The real-space lattice reconstruction enables the discovery and identification of structural motifs associated with vacancies, inclusions, screw dislocations, and grain boundaries that reveal stark similarities between nanoparticle assemblies and their counterparts in atomic crystals. Through a volumetric particle-by-particle probing of superlattices, this study sheds light on the relationship between a systems design, assembly process, and a resulting 3D nanoparticle organization. In this talk we will highlight the developed method, and its application to revealing 3D structure of nanoparticle-based materials.